Book Image

Hands-On Computer Vision with Detectron2

By : Van Vung Pham
5 (4)
Book Image

Hands-On Computer Vision with Detectron2

5 (4)
By: Van Vung Pham

Overview of this book

Computer vision is a crucial component of many modern businesses, including automobiles, robotics, and manufacturing, and its market is growing rapidly. This book helps you explore Detectron2, Facebook's next-gen library providing cutting-edge detection and segmentation algorithms. It’s used in research and practical projects at Facebook to support computer vision tasks, and its models can be exported to TorchScript or ONNX for deployment. The book provides you with step-by-step guidance on using existing models in Detectron2 for computer vision tasks (object detection, instance segmentation, key-point detection, semantic detection, and panoptic segmentation). You’ll get to grips with the theories and visualizations of Detectron2’s architecture and learn how each module in Detectron2 works. As you advance, you’ll build your practical skills by working on two real-life projects (preparing data, training models, fine-tuning models, and deployments) for object detection and instance segmentation tasks using Detectron2. Finally, you’ll deploy Detectron2 models into production and develop Detectron2 applications for mobile devices. By the end of this deep learning book, you’ll have gained sound theoretical knowledge and useful hands-on skills to help you solve advanced computer vision tasks using Detectron2.
Table of Contents (20 chapters)
1
Part 1: Introduction to Detectron2
4
Part 2: Developing Custom Object Detection Models
12
Part 3: Developing a Custom Detectron2 Model for Instance Segmentation Tasks
15
Part 4: Deploying Detectron2 Models into Production

Applying Train-Time and Test-Time Image Augmentations

The previous chapter introduced the existing augmentation and transformation classes Detectron2 offers. This chapter introduces the steps to apply these existing classes to training. Additionally, Detectron2 offers many image augmentation classes. However, they all work on annotations from a single input at a time, while modern techniques may need to combine annotations from different inputs while creating custom augmentations. Therefore, this chapter also provides the foundation for Detectron2’s data loader component. Understanding this component helps explain how to apply existing image augmentations and modify existing codes to implement custom techniques that need to load data from different inputs. Finally, this chapter details the steps for applying image augmentations during test time to improve accuracy.

By the end of this chapter, you will be able to understand how Detectron2 loads its data, how to apply existing...